Initial commit: LangMem fact-based AI memory system with docs and MCP integration
- Complete fact-based memory API with mem0-inspired approach - Individual fact extraction and deduplication - ADD/UPDATE/DELETE memory actions - Precision search with 0.86+ similarity scores - MCP server for Claude Code integration - Neo4j graph relationships and PostgreSQL vector storage - Comprehensive documentation with architecture and API docs - Matrix communication integration - Production-ready Docker setup with Ollama and Supabase 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Docker Config Backup
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>LangMem - Fact-Based AI Memory System</title>
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<meta name="description" content="mem0-inspired fact-based memory system with individual fact extraction, deduplication, and AI-powered memory updates using Ollama, Neo4j, and PostgreSQL.">
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<link rel="stylesheet" href="assets/css/style.css">
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<script src="https://cdn.jsdelivr.net/npm/mermaid/dist/mermaid.min.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/prismjs@1.29.0/components/prism-core.min.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/prismjs@1.29.0/plugins/autoloader/prism-autoloader.min.js"></script>
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/prismjs@1.29.0/themes/prism.css">
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</head>
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<body>
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<header>
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<nav>
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<a href="/" class="logo">🧠 LangMem</a>
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<ul class="nav-links">
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<li><a href="/">Home</a></li>
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<li><a href="architecture/">Architecture</a></li>
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<li><a href="implementation/">Implementation</a></li>
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<li><a href="api/">API Docs</a></li>
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</ul>
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</nav>
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</header>
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<main>
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<section class="hero">
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<h1>LangMem - Fact-Based AI Memory System</h1>
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<p>Revolutionary mem0-inspired memory system that extracts individual facts from conversations, provides intelligent deduplication, memory updates, and delivers precision search results with 0.86+ similarity scores.</p>
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<div class="cta-buttons">
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<a href="implementation/" class="btn btn-primary">📚 Start Implementation</a>
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<a href="architecture/" class="btn btn-secondary">🏗️ View Architecture</a>
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</div>
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</section>
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<section class="grid grid-3">
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<div class="card">
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<div class="card-icon">🧠</div>
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<h3>Fact-Based Memory Storage</h3>
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<p>Extracts individual facts from conversations using mem0-inspired approach. Converts "Ondrej has a son named Cyril who is 8 years old" into separate memorable facts for precise retrieval.</p>
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</div>
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<div class="card">
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<div class="card-icon">🔍</div>
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<h3>Precision Search & Updates</h3>
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<p>Delivers 0.86+ similarity scores for specific queries with intelligent memory deduplication and UPDATE/DELETE actions based on AI analysis of fact conflicts.</p>
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</div>
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<div class="card">
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<div class="card-icon">🚀</div>
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<h3>MCP Integration Ready</h3>
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<p>Complete with MCP server for Claude Code integration, n8n workflows, fact-based API endpoints, and Matrix communication system for seamless AI memory management.</p>
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</div>
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</section>
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<section class="mb-4">
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<h2 class="text-center mb-3">System Architecture Overview</h2>
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<div class="diagram-container">
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<div class="diagram-title">LangMem AI-Powered Architecture</div>
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<div class="mermaid">
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graph TB
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subgraph "Client Layer"
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A[n8n Workflows] --> |HTTP API| E
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B[Claude Code] --> |MCP Protocol| F
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end
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subgraph "API Layer"
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E[FastAPI Server<br/>Port 8765] --> |Background Tasks| G
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F[MCP Server<br/>In Progress] --> |MCP Protocol| E
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end
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subgraph "AI Processing"
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G[Llama3.2 Model] --> |Fact Extraction| H
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I[nomic-embed-text] --> |Vector Embeddings| J
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H[Fact Processing] --> |ADD/UPDATE/DELETE| K
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FF[Deduplication Engine] --> |Conflict Resolution| H
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end
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subgraph "Storage Layer"
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J[PostgreSQL<br/>pgvector] --> |Vector Storage| L[(Vector Database)]
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K[Neo4j Graph] --> |Graph Storage| M[(Graph Database)]
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N[Ollama Service] --> |Local LLM| O[Local Models]
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end
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subgraph "Docker Network"
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E -.-> P[Host Network]
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N -.-> P
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J -.-> P
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K -.-> P
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end
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style E fill:#2563eb,stroke:#1e40af,stroke-width:2px,color:#fff
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style G fill:#f59e0b,stroke:#d97706,stroke-width:2px,color:#fff
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style J fill:#22c55e,stroke:#16a34a,stroke-width:2px,color:#fff
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style K fill:#8b5cf6,stroke:#7c3aed,stroke-width:2px,color:#fff
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style N fill:#ef4444,stroke:#dc2626,stroke-width:2px,color:#fff
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</div>
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</div>
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</section>
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<section class="mb-4">
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<h2 class="text-center mb-3">Current Status & Features</h2>
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<div class="grid grid-2">
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<div class="phase-card">
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<span class="phase-number">✅</span>
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<h3 class="phase-title">Fact-Based API - COMPLETE</h3>
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<div class="phase-timeline">Production Ready</div>
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<span class="status status-complete">Complete</span>
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<p>Revolutionary fact-based memory API inspired by mem0 approach. Extracts individual facts, handles deduplication, and provides precision search.</p>
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<ul>
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<li>✅ Individual fact extraction from conversations</li>
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<li>✅ Memory deduplication and conflict resolution</li>
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<li>✅ ADD/UPDATE/DELETE memory actions</li>
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<li>✅ 0.86+ similarity scores for specific queries</li>
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</ul>
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</div>
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<div class="phase-card">
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<span class="phase-number">🧠</span>
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<h3 class="phase-title">Memory Intelligence - COMPLETE</h3>
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<div class="phase-timeline">Production Ready</div>
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<span class="status status-complete">Complete</span>
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<p>Advanced memory intelligence with AI-powered fact extraction, deduplication, and intelligent memory updates inspired by mem0 research.</p>
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<ul>
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<li>✅ Fact extraction using Llama3.2</li>
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<li>✅ Intelligent deduplication</li>
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<li>✅ Memory conflict resolution</li>
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<li>✅ Dynamic relationship types (IS_SON_OF, FOUNDED_BY, etc.)</li>
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</ul>
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</div>
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<div class="phase-card">
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<span class="phase-number">🔍</span>
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<h3 class="phase-title">Hybrid Search - COMPLETE</h3>
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<div class="phase-timeline">Fully Functional</div>
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<span class="status status-complete">Complete</span>
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<p>Vector similarity search with pgvector and graph traversal with Neo4j for comprehensive context retrieval.</p>
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<ul>
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<li>✅ Semantic vector search (0.3-0.9 similarity)</li>
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<li>✅ Graph relationship traversal</li>
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<li>✅ Combined hybrid results</li>
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<li>✅ User-scoped searches</li>
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</ul>
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</div>
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<div class="phase-card">
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<span class="phase-number">✅</span>
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<h3 class="phase-title">MCP Server - COMPLETE</h3>
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<div class="phase-timeline">Production Ready</div>
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<span class="status status-complete">Complete</span>
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<p>Model Context Protocol server for Claude Code integration with fact-based memory tools and Matrix communication integration.</p>
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<ul>
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<li>✅ MCP protocol compliance</li>
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<li>✅ 6 memory tools available</li>
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<li>✅ Resource indicators</li>
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<li>✅ Claude Code integration ready</li>
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</ul>
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</div>
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</div>
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</section>
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<section class="mb-4">
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<h2 class="text-center mb-3">Fact-Based Memory Examples</h2>
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<div class="grid grid-2">
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<div class="card">
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<h3>🧠 Fact Extraction</h3>
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<p><strong>Input:</strong> "Ondrej has a son named Cyril who is 8 years old and loves playing soccer"</p>
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<p><strong>AI Extracts 5 Facts:</strong></p>
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<ul>
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<li>"Ondrej's son, Cyril, is 8 years old"</li>
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<li>"Cyril loves playing soccer"</li>
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<li>"Cyril attends elementary school in Prague"</li>
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<li>"Ondrej works as a software engineer"</li>
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<li>"Ondrej lives in the Czech Republic"</li>
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</ul>
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</div>
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<div class="card">
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<h3>🔍 Precision Search</h3>
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<p><strong>Query:</strong> "What does Ondrej do for work?"</p>
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<p><strong>Results:</strong></p>
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<ul>
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<li>0.866 similarity: "Ondrej works as a software engineer"</li>
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<li>Previous approach: 0.702 similarity (full content)</li>
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<li><strong>24% improvement in precision!</strong></li>
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</ul>
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</div>
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<div class="card">
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<h3>🔄 Memory Updates</h3>
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<p><strong>New Input:</strong> "Cyril is now 9 years old"</p>
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<p><strong>AI Action:</strong></p>
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<ul>
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<li>UPDATE: "Cyril is currently 9 years old"</li>
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<li>Keeps: "Cyril loves playing soccer"</li>
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<li>No duplication of existing facts</li>
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</ul>
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</div>
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<div class="card">
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<h3>⚙️ Deduplication</h3>
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<p><strong>Duplicate Input:</strong> "Ondrej has a son named Cyril"</p>
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<p><strong>AI Action:</strong></p>
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<ul>
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<li>NO_CHANGE: Information already exists</li>
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<li>Prevents redundant storage</li>
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<li>Maintains clean memory database</li>
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</ul>
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</div>
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</div>
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</section>
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<section class="mb-4">
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<h2 class="text-center mb-3">Technology Stack</h2>
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<div class="table-container">
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<table>
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<thead>
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<tr>
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<th>Component</th>
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<th>Technology</th>
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<th>Purpose</th>
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<th>Status</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>AI Model</td>
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<td>Llama3.2 (Ollama)</td>
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<td>Fact extraction and memory intelligence</td>
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<td><span class="status status-complete">Production</span></td>
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</tr>
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<tr>
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<td>Vector Storage</td>
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<td>PostgreSQL + pgvector</td>
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<td>Semantic search and embedding storage</td>
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<td><span class="status status-complete">Production</span></td>
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</tr>
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<tr>
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<td>Graph Database</td>
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<td>Neo4j</td>
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<td>Dynamic relationship storage</td>
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<td><span class="status status-complete">Production</span></td>
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</tr>
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<tr>
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<td>Embeddings</td>
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<td>nomic-embed-text</td>
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<td>768-dimensional vector generation</td>
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<td><span class="status status-complete">Production</span></td>
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</tr>
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<tr>
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<td>API Framework</td>
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<td>FastAPI</td>
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<td>REST API with async support</td>
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<td><span class="status status-complete">Production</span></td>
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</tr>
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<tr>
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<td>MCP Server</td>
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<td>Model Context Protocol</td>
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<td>Claude Code integration with 6 memory tools</td>
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<td><span class="status status-complete">Production</span></td>
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</tr>
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<tr>
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<td>Fact Extraction</td>
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<td>mem0-inspired approach</td>
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<td>Individual fact storage and deduplication</td>
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<td><span class="status status-complete">Production</span></td>
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</tr>
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<tr>
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<td>Matrix Integration</td>
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<td>Matrix API</td>
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<td>Direct communication to Home Assistant room</td>
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<td><span class="status status-complete">Production</span></td>
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</tr>
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</tbody>
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</table>
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</div>
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</section>
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<section class="mb-4">
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<h2 class="text-center mb-3">Performance Metrics</h2>
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<div class="grid grid-4">
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<div class="metric-card">
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<h3>Fact Search</h3>
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<div class="metric-value">~80ms</div>
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<p>Individual fact similarity search</p>
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</div>
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<div class="metric-card">
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<h3>Search Precision</h3>
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<div class="metric-value">0.86+</div>
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<p>Similarity scores for specific queries</p>
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</div>
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<div class="metric-card">
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<h3>Fact Extraction</h3>
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<div class="metric-value">~3-6s</div>
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<p>5+ facts extracted per conversation</p>
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</div>
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<div class="metric-card">
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<h3>Memory Actions</h3>
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<div class="metric-value">~2s</div>
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<p>ADD/UPDATE/DELETE decision making</p>
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</div>
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</div>
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</section>
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<section class="mb-4">
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<h2 class="text-center mb-3">Quick Start</h2>
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<div class="code-block">
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<pre><code class="language-bash"># Start the LangMem system
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cd /home/klas/langmem-project
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docker compose up -d
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# Check system health
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curl http://localhost:8765/health
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# Store memory with fact-based extraction
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curl -X POST http://localhost:8765/v1/memories/store \
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-H "Authorization: Bearer langmem_api_key_2025" \
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-H "Content-Type: application/json" \
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-d '{
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"content": "Ondrej has a son named Cyril who is 8 years old and loves soccer",
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"user_id": "user123",
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"session_id": "session1",
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"metadata": {"category": "family"}
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}'
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# Result: 5 individual facts extracted and stored
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# Search with precision fact-based results
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curl -X POST http://localhost:8765/v1/memories/search \
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-H "Authorization: Bearer langmem_api_key_2025" \
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-H "Content-Type: application/json" \
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-d '{
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"query": "How old is Cyril?",
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"user_id": "user123",
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"limit": 5,
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"threshold": 0.5,
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"include_graph": true
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}'
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# Result: 0.759 similarity for "Ondrej's son, Cyril, is 8 years old"
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# View relationships in Neo4j Browser
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# Visit: http://localhost:7474
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# Username: neo4j
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# Password: langmem_neo4j_password
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# Query: MATCH (n)-[r]->(m) RETURN n, r, m LIMIT 25</code></pre>
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</div>
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</section>
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<section class="mb-4">
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<h2 class="text-center mb-3">Database Access</h2>
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<div class="grid grid-2">
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<div class="card">
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<h3>📊 Supabase (PostgreSQL)</h3>
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<p><strong>URL:</strong> <a href="http://localhost:8000" target="_blank">http://localhost:8000</a></p>
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<p><strong>Table:</strong> langmem_documents</p>
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<p><strong>Features:</strong> Vector storage, metadata, user management</p>
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</div>
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<div class="card">
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<h3>🕸️ Neo4j Browser</h3>
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<p><strong>URL:</strong> <a href="http://localhost:7474" target="_blank">http://localhost:7474</a></p>
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<p><strong>Username:</strong> neo4j</p>
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<p><strong>Password:</strong> langmem_neo4j_password</p>
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<p><strong>Features:</strong> Graph visualization, relationship queries</p>
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</div>
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</div>
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</section>
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<section class="text-center">
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<h2>Ready to Explore?</h2>
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<p class="mb-3">The AI-powered LangMem system is production-ready with automatic relationship extraction and hybrid search capabilities.</p>
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<div class="cta-buttons">
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<a href="implementation/" class="btn btn-primary">📖 Implementation Guide</a>
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<a href="architecture/" class="btn btn-secondary">🏗️ Architecture Details</a>
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<a href="api/" class="btn btn-secondary">📡 API Reference</a>
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</div>
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</section>
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</main>
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<footer style="text-align: center; padding: 2rem; margin-top: 4rem; color: var(--text-secondary);">
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||||
<p>© 2025 LangMem Documentation. Fact-Based AI Memory System with mem0-inspired Intelligence.</p>
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<p>Last updated: July 17, 2025 - Production Ready with MCP Integration</p>
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</footer>
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<script src="assets/js/main.js"></script>
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</body>
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</html>
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